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首页> 外文期刊>JMIR public health and surveillance. >Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts
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Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

机译：在Covid-19疫苗接种中检查社交媒体的效用：672,133 Twitter帖子的无监督学习

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Background Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. Objective The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. Methods Twitter was searched for tweets related to “COVID-19” and “vaccine” over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). Results Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. Conclusions The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.
机译：背景技术虽然Covid-19疫苗最近可用，但全球大规模疫苗接种的努力可能受到疫苗犹豫不决的疫苗问题的阻碍。目的本研究的目的是利用社交媒体数据来捕获关于Covid-19疫苗的近距离的公共观点和情绪，有意理解捕获公众关注的关键问题以及障碍和促进者成功的Covid-19疫苗接种。方法在新闻稿关于第一个有效疫苗的新闻稿之后，将在11周后，在11周后，在11周期间，研究了Twitter。无监督的机器学习方法（即结构主题建模）用于识别推文的主题，每个主题进一步使用手动进行主题分析将主题分组，并由COM-B的理论框架引导（能力，机会，和动机的行为组成部分）模型。使用规则的机器学习模型VADER（价值意识词典和情绪推理仪）也进行了推文的情感分析。结果与Covid-19疫苗相关的推文被世界各地的个人发布（n = 672,133）。确定了六个总体主题：（1）与Covid-19疫苗相关的情绪反应（19.3％），（2）与Covid-19疫苗相关的公共关注（19.6％），（3）关于与Covid-19相关的新闻项目的讨论疫苗（13.3％），（4）关于Covid-19疫苗的公共卫生通信（10.3％），（5）关于Covid-19疫苗接种驱动方法（17.1％）和（6）关于Covid分布的讨论19疫苗（20.3％）。具有负面情绪的推文在很大程度上落在了情绪反应和与Covid-19疫苗相关的公众关注的主题中。与疫苗接种的促进者相关的推文随着时间的推移而显示时间变化，而在整个研究期间，与障碍有关的推文仍然在很大程度上。结论本研究的调查结果可以促进制定全面策略，以改善Covid-19疫苗摄取;它们突出了在Covid-19疫苗接种规划中需要注意的关键流程，并提供关于在持续接种疫苗接种驱动器中的不断变化的障碍和辅助者的反馈，以允许进一步的政策调整。调查结果还说明了社交媒体在Covid-19疫苗接种中的三个关键作用，如下：监督和监测，通信平台和政府反应的评估。

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